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Record W2017249660 · doi:10.1016/j.arthro.2009.11.023

Anterior Cruciate Ligament Graft Fixation—A Myth Busted?

2010· review· en· W2017249660 on OpenAlexaff
Teppo L. N. Järvinen, Ghassan Alami, Jón Karlsson

Bibliographic record

VenueArthroscopy The Journal of Arthroscopic and Related Surgery · 2010
Typereview
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsUniversity of British Columbia
FundersPirkanmaan SairaanhoitopiiriSigrid Juséliuksen Säätiö
KeywordsFixation (population genetics)Anterior cruciate ligamentMedicineAnatomySurgery

Abstract

fetched live from OpenAlex

Anterior cruciate ligament graft fixation has become one of the most investigated topics in the sports traumatology literature. With over 400 publications within the past decade, a plausible explanation for the popularity of the topic would be that anterior cruciate ligament graft fixation represents an obvious clinical problem. Yet this does not seem to be the case. We set out to analyze the veracity of the notion that the fixation site is the weak link in a reconstructed knee in the early postoperative period. A mere temporal association is found between the first clinical reports on increased anterior tibial translation relative to the femur with soft-tissue grafts and the first pullout studies reporting lower ultimate failure loads with such grafts. This association was sufficient to convince the orthopaedic community at large that actual causality exists between soft-tissue graft fixation failure and increased knee laxity during healing. Thus the concept of "graft slippage" was born. Even with the imminent risk of being misconstrued as contentious, we submit that the entire concept of graft slippage is a myth, founded on poor scientific practice and affected by commercial bias. As a way forward, clinically important phenomena should be demonstrated through experiments with clear and sound clinical endpoints. As for preclinical studies, although they are indisputably helpful in the elaboration of such phenomena, serious hazards lie in declaring them a sufficient scientific basis for new research or, worse, for clinical standards of care. More importantly, no matter how sophisticated or fascinating their methodology, preclinical studies do not relieve us from the necessity and duty of proving our theories, whenever possible, with randomized controlled trials.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.032
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0030.001
Science and technology studies0.0030.028
Scholarly communication0.0080.023
Open science0.0030.005
Research integrity0.0150.030
Insufficient payload (model declined to judge)0.0050.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.314
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2010
Admission routes1
Has abstractyes

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